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Review of Current Patterns of Treatment and Costs in Follicular Non-Hodgkin’s Lymphoma

2008· article· en· W2592808937 on OpenAlexaboutno aff
Talia Foster, Jeffrey D. Miller, Mark E. Boye, Mason W. Russell

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsFollicular lymphomaMedicineLymphomaIncidence (geometry)MalignancyDemographyDiseaseOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction Follicular non-Hodgkin’s lymphoma (FNHL) comprises about 15–30% of all incident NHL in developed countries. US SEER data show that the annual incidence rate per 100,000 persons is 3.0 and 1.3 (white and black males, respectively) and 2.7 and 0.9 (white and black females, respectively). Patients with this slow-growing malignancy may live for 10 years or more, but it is considered incurable with standard treatment. The common disease pattern is a series of relapses and remissions, with each relapse responding less to treatment, and each remission shorter than the preceding one. Little is known about patterns and costs of current treatment for FNHL. Methods. We conducted a systematic review of the English-language, MEDLINE-indexed literature on FNHL published during the 10-year period beginning in May 1997 and ending in May 2007. The following search algorithm was used, with keywords in the title or abstract: (nodular OR follicular) AND non-Hodgkin‘s AND lymphoma[ti] NOT PCR NOT „case reports“[pt]. Government and organization-sponsored websites also were searched using these keywords. No geographic restrictions were used. Results: Literature identified. We manually reviewed the initial search results of 375 articles to identify those relevant to this research on FNHL treatment patterns and costs. Authors of 16 primary research studies reported treatment patterns (5) and/or costs (15). The 5 studies of treatment patterns were conducted in the UK (2), Canada (1), the Netherlands (1), and the US (1); the 15 economic studies were conducted in the US (5), UK (3), Canada (2), Spain (2), France (1), Germany (1), and the Netherlands (1). Nine of the 16 studies were published as abstracts from professional meetings (1 reporting treatment, all 9 reporting costs). Results: Treatment patterns. Studies reporting primary data on FNHL treatment patterns tended to assess these data during the 1990s, prior to the adoption of rituximab + chemotherapy as the current standard of treatment. No more than 13 patients received rituximab in each of these studies. Of the 5 studies of treatment patterns we identified, 1 collected data through 1998, 2 through 2001, and 1 did not collect drug-specific data. One UK study reported treatment patterns through August 2003, by which date 6 patients had received rituximab. Results: Costs. The 15 cost studies identified comprised 3 cost analyses using databases (2 US, 1 UK), 2 primary studies of costs (1 US, 1 Netherlands), and 1 primary study assessing work impact (Canada). There were also 8 economic models, including 6 cost-effectiveness analyses (1 with two publications), 1 budgetary impact model, and 1 model calculating total treatment costs. Most of these 15 economic studies focus on rituximab and fludarabine. Findings suggest that the high initial cost of rituximab is offset by its low incidence of adverse events, producing equivalent average annual direct costs to those of fludarabine. Maintenance with rituximab is cost-effective versus observation alone, and the addition of rituximab to systemic chemotherapy is cost-effective versus chemotherapy alone. The single study of indirect costs (work loss) indicates highest costs among patients receiving systemic therapy. Available economic data in FNHL do not include broader societal impact of the disease. Conclusions. Although the introduction of biological therapies such as rituximab has shifted the treatment paradigm for FNHL, few studies published in the last 10 years have evaluated treatment patterns and costs of this disease. As new treatments enter the market, primary cost data and other economic information about FNHL will be needed to evaluate their relative cost-effectiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0280.037
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.290
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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